Journal of Green Energy Research and Innovation

Abstract Renewable energy hubs have the potential to significantly improve the technical performance of microgrids while reducing environmental pollutants. This is achieved through efficient energy management within the hubs and determining their optimal capacities and placements in the energy network. This article focuses on the planning and operation of renewable energy hubs integrated with storage systems in microgrids. The objective is to minimize the overall costs related to building resources and storage facilities within these hubs. Key constraints addressed include power flow equations, operational limitations, and the planning-operational model of the hubs. The innovation of this approach lies in combining a comprehensive planning-operation model for renewable energy hubs with the implementation of a bio-waste unit model. The numerical results underscore the effectiveness of this strategy, demonstrating improvements in microgrid performance through efficient hub planning and operation. Specifically, the optimal planning process achieved the lowest construction costs for the hubs, while the optimal operation led to substantial reductions in energy losses and voltage drops within the microgrid by approximately 33.8% and 51.3%, respectively, in comparison with traditional power flow analysis. In this condition for the used case study, the planning cost of EHs is M$46.43.‎

Abstract supporting industrial and societal growth. Power generation is carried out through various sources such as solar energy, wind, and thermal systems, each with unique infrastructural demands and environmental impacts. Assessing the environmental consequences of constructing power plants is essential for minimizing harm to ecosystems and communities. This study aims to identify optimal locations for establishing electricity generation facilities by evaluating both infrastructure requirements and environmental considerations. A comprehensive site selection process is conducted, taking into account the characteristics of different types of power plants. Cost estimation for each potential site includes factors such as land acquisition and is tailored to the type and capacity of the plant. Environmental variables are also incorporated into the analysis to provide a more holistic view of each option. Using GAMS optimization software, the study identifies the most cost-effective and environmentally responsible sites for power plant installation and recommends the most suitable capacities for each selected location. The findings contribute to more sustainable energy planning and informed decision-making in the energy sector.

Abstract This study presents a hybrid CNN-LSTM model for short-term energy consumption prediction in Iranian residential buildings, focusing on Tehran. By integrating multimodal data, meteorological, temporal, occupancy proxies, and building metadata, and employing deep feature engineering via a stacked denoising autoencoder, the model achieves high accuracy (R² = 0.89) and robustness against data imperfections. The framework demonstrates the critical role of cultural and contextual features, such as Iranian holidays, in enhancing prediction validity. SHAP analysis provides interpretability, aligning model logic with local realities. The results offer a scalable, context-aware solution for intelligent energy management in Iran’s urban environment.‎

Abstract This study reviews approaches and models for wind turbines (WT) and tower grounding ‎systems and their protection against lightning, based on a core set of approximately sixty ‎‎(~60) grounding-focused scientific articles. This article also deals with various aspects such ‎as the dependence of electrical properties of soil on frequency, the effect of ionization, ‎heterogeneity and classification of soil and the optimal position of electrodes as well as the ‎connection arrangements of WTs in a field. This review also deals with the effects of direct ‎and indirect lightning on turbines, voltage distribution between turbines and the behavior of ‎the earth system at different frequencies. At the end, the points in the reviewed articles are ‎in the form of diagrams for a better understanding of the various topics discussed and a ‎comprehensive view. This work shows the limitations and study gaps of previous works with ‎recommendations for the evolution of lightning protection systems for WTs and tall ‎electrical structures.‎‎

Abstract This paper presents a comprehensive analysis of the joint distribution of electrical and thermal ‎energy in a smart home connected to a microgrid integrating renewable resources, combined heat and ‎power (CHP) units, and storage systems. While most existing studies have primarily focused on ‎generation planning and storage management, fewer works have examined the simultaneous ‎optimization of electricity and heat flows in residential environments. To address this gap, a mixed-‎integer linear programming (MILP) model is developed to schedule household appliances and manage ‎load shifting according to time-varying electricity prices, heating demand, and demand profiles, and the ‎optimization is solved using MATLAB tools. The proposed framework is applied to a residential ‎complex of 10 and 20 households under different CHP capacities and demand scenarios. Simulation ‎results reveal that increasing CHP capacity from 5 kW to 20 kW significantly improves the coordination ‎of electrical and thermal distribution, reduces reliance on the main grid, and lowers boiler operation, ‎thereby enhancing overall efficiency. Additional analyses with different numbers of households confirm ‎the scalability of the model, ensuring stable performance under varying load levels. A comparative ‎scenario without microgrid integration further highlights the substantial benefits of the proposed ‎system in reducing operational costs and improving resilience. To address this gap, a unified MILP ‎jointly schedules electrical–thermal resources (CHP, renewables, and dual electrical/thermal storage) ‎and employs a price–energy iterative scheme with explicit fairness constraints, ensuring no household is ‎worse off than in non-cooperative operation. These results demonstrate that the coordinated ‎consideration of both power and heat flows provides a more holistic strategy for smart home energy ‎management than electricity-only approaches. In 24-h studies, increasing installed CHP capacity from 5 ‎to 100 kW reduced total operating cost from $379.68 to $191.52 (≈49.6%). By integrating demand ‎response (DR) programs with CHP and renewable resources, the proposed method reduces energy costs, ‎strengthens supply security, and contributes to the sustainability of residential microgrids.‎

Abstract Dropwise condensation (DWC) increases phase change heat transfer efficiency, enabling more energy efficient thermal processes that directly support greener energy technologies and help lower overall carbon emissions. Correspondingly, this research work presents a numerical assessment of isolated R134a droplet during DWC on solid surfaces, focusing on the coupled impacts of Marangoni convection, contact angle across horizontal ( 𝛽 = 0°) and vertical (𝛽 = 90° ) surfaces to investigate their impact on heat transfer during DWC. In this regard, droplet geometry was modeled utilizing Surface Evolver, while computational fluid dynamics (CFD) simulations were performed in ANSYS FLUENT with a pressure-based solver and Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm. The simulation outcomes were validated through comparison with established theoretical models and previously published experimental measurements. Regarding the results, greater Marangoni numbers (𝑀𝑎 ) enhance internal circulation, leading to more uniform temperature distributions and increased heat transfer coefficients. Also, contact angle was found to positively influence average heat flux (AHF) by reducing liquid–solid contact area, while vertical surfaces consistently exhibited higher AHF because of smaller droplet footprints. The results indicate that at and , the vertical surface yields an AHF that is nearly 4.5% superior than that of the horizontal surface. Furthermore, unlike previous studies, which primarily examined these phenomena in general condensation contexts, this work specifically addresses their implications for refrigeration systems. By investigating R134a droplets, the findings provide novel insights into droplet scale condensation mechanisms that can contribute to reducing energy consumption in refrigerators.‎‎‎